compact
Class: TreeBagger
Compact ensemble of decision trees
Description
CMdl = compact(Mdl)Mdl, a TreeBagger model object. You can predict
regressions using CMdl exactly as you can using Mdl.
However, since CMdl does not contain training data,
you cannot perform some actions, such as make out-of-bag predictions
using oobPredict.
Input Arguments
|
A regression ensemble created with |
Output Arguments
|
A compact regression ensemble. |
Examples
Reduce Size of Bag of Trees
Create a compact bag of trees for efficiently making predictions on new data.
Load the ionosphere data set.
load ionosphereTrain a bag of 100 classification trees using all measurements and the AdaBoostM1 method.
Mdl = TreeBagger(100,X,Y,'Method','classification')
Mdl =
TreeBagger
Ensemble with 100 bagged decision trees:
Training X: [351x34]
Training Y: [351x1]
Method: classification
NumPredictors: 34
NumPredictorsToSample: 6
MinLeafSize: 1
InBagFraction: 1
SampleWithReplacement: 1
ComputeOOBPrediction: 0
ComputeOOBPredictorImportance: 0
Proximity: []
ClassNames: 'b' 'g'
Properties, Methods
Mdl is a TreeBagger model object that contains the training data, among other things.
Create a compact version of Mdl.
CMdl = compact(Mdl)
CMdl =
CompactTreeBagger
Ensemble with 100 bagged decision trees:
Method: classification
NumPredictors: 34
ClassNames: 'b' 'g'
Properties, Methods
CMdl is a CompactTreeBagger model object. CMdl is almost the same as Mdl. One exception is that it does not store the training data.
Compare the amounts of space consumed by Mdl and CMdl.
mdlInfo = whos('Mdl'); cMdlInfo = whos('CMdl'); [mdlInfo.bytes cMdlInfo.bytes]
ans = 1×2
1115742 976936
Mdl consumes more space than CMdl.
CMdl.Trees stores the trained classification trees (CompactClassificationTree model objects) that compose Mdl.
Display a graph of the first tree in the compact model.
view(CMdl.Trees{1},'Mode','graph');
By default, TreeBagger grows deep trees.
Predict the label of the mean of X using the compact ensemble.
predMeanX = predict(CMdl,mean(X))
predMeanX = 1x1 cell array
{'g'}